(1) Field of Invention
The present invention relates to a system for object cueing in motion imagery and, more particularly, to a system for object cueing in motion imagery using bio-inspired features for frame-to-reference registration.
(2) Description of Related Art
There are many different approaches to detecting objects of interest in motion imagery. The exhaustive search approach is to train a classifier and run an exhaustive scan over a predefined object window. However, this approach produces a lot of false alarms depending on classification performance. Furthermore, this approach is a computationally expensive method and also suffers from being unable to detect objects that are dissimilar to training examples. Previous pixel-level change detection methods, such as background subtraction, inter-frame differencing, and three frame differencing, are widely used (see Literature Reference Nos. 1 and 2).
Background subtraction relies on a background model for comparison, but adaptive background updating is costly for a moving camera. Inter-frame differencing easily detects motion but does a poor job of localizing the object (i.e., usually only parts of the object are detected). Specifically, inter-frame differencing only detects leading/trailing edges of translating objects with uniform color. Three-frame differencing uses future, current, and previous image frames to detect motion but can coarsely localize the object only if a suitable frame lag is adopted.
The closest prior art to the present invention includes U.S. Pat. No. 7,697,725, entitled, “Method and apparatus for autonomous object tracking” and U.S. Patent Publication No. 2011/0142283, entitled, “Apparatus and method for moving object detection.” These prior methods describe an apparatus and method to detect moving objects by computing a corresponding frame difference for every two successive image frames of a moving object, and segmenting a current image frame of the two successive image frames into a plurality of homogeneous regions. The system gradually merges the computed frame differences via a morphing-based technique to obtain the location of the moving object. These prior methods are prone to noise in features to be used in estimating global motion compensation. The implemented image alignment or frame-to-reference registration module is usually very time-consuming and not good for real-time operations. Also, in order to achieve the best performances, they require manual parameter tuning, which is a tedious job and prevents rapid field deployment.
Each of the prior methods described above exhibit limitations that make them incomplete. Thus, a continuing need exists for a method that can be used to cue moving objects of interest in motion imagery captured either from stationary or mobile sensors, estimate parameters automatically, and reduce searching time, making the whole surveillance system work in real-time.